Detection of Mesoscale Eddies using Structural Statistical Feature
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Baomin Shao, Hongyun Jia|
|DOI : 10.14445/22312803/IJCTT-V69I7P108|
How to Cite?
Baomin Shao, Hongyun Jia, "Detection of Mesoscale Eddies using Structural Statistical Feature," International Journal of Computer Trends and Technology, vol. 69, no. 7, pp. 52-57, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I7P108
Because of the size and the increasing quantity of remote sensing marine images, tools are needed for computer-aided research. Automated eddy detection methods are fundamental tools to study mesoscale eddies from the large datasets derived from satellite images. In this work, mesoscale ocean eddies are characterized by currents that flow in a roughly circular motion around the center of the eddy, the sense of rotation of these currents may either be cyclonic or anticyclonic, and a new algorithm is presented to detect eddy centers based on a supervised learning method using support vector machines. A structural statistical feature (SSF) kernel function is introduced in order to favor the distinction between eddy centers and other points. The method learns a generic model for mesoscale eddy by using the SSF of training examples. The algorithm has been applied to over 24 years of AVISO MSLA data derived from current sea data in the global area.
Eddy detection, Kernel methods, SVM, MSLA, Sea current.
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